Estimating the time until a reply email will be received using a recipient behavior model

ABSTRACT

A system is provided. The system includes a recipient behavior model for generating an estimate of a receipt time of a reply email from a recipient of an initial email by applying machine learning to the initial email and to training data from other emails. The system further includes an indicator device for indicating the estimate to a user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation application of co-pending U.S. patentapplication Ser. No. 13/627,075 filed on Sep. 26, 2012, incorporatedherein by reference in its entirety.

BACKGROUND

1. Technical Field

The present principles relate generally to electronic mail and, inparticular, to estimating the time until a reply email will be receivedusing a recipient behavior model.

2. Description of the Related Art

For a variety of reasons, email remains extremely popular for bothbusiness and personal communication. However, presently, when a usersends an email, he or she has little or no information on the time untila reply will be received.

SUMMARY

According to an aspect of the present principles, there is provided amethod in an email communication system having at least a processor. Themethod includes generating an estimate of a receipt time of a replyemail from a recipient of an initial email using a recipient behaviormodel that applies machine learning to the initial email and to trainingdata from other emails. The method further includes indicating theestimate to a user using an indication device.

According to another aspect of the present principles, there is provideda computer program product for providing an estimate of a receipt timeof a reply email from a recipient of an initial email. The computerprogram product includes a computer readable storage medium havingprogram code embodied therewith. The program code is executable by acomputer to perform a method. The method includes generating theestimate of the receipt time of the reply email from the recipient ofthe initial email using a recipient behavior model that applies machinelearning to the initial email and to training data from other emails.The method further includes indicating the estimate to a user using anindication device.

According to yet another aspect of the present principles, there isprovided a system. The system includes a recipient behavior model forgenerating an estimate of a receipt time of a reply email from arecipient of an initial email by applying machine learning to theinitial email and to training data from other emails. The system furtherincludes an indicator device for indicating the estimate to a user.

These and other features and advantages will become apparent from thefollowing detailed description of illustrative embodiments thereof,which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

The disclosure will provide details in the following description ofpreferred embodiments with reference to the following figures wherein:

FIG. 1 shows an exemplary processing system 100 to which the presentprinciples may be applied, in accordance with an embodiment of thepresent principles;

FIG. 2 shows an exemplary system 200 for estimating a receipt time of areply email using a recipient behavior model, in accordance with anembodiment of the present principles;

FIG. 3 shows another exemplary system 300 for estimating a receipt timeof a reply email using a recipient behavior model, in accordance with anembodiment of the present principles;

FIG. 4 shows an exemplary method 400 for estimating a receipt time of areply email using a recipient behavior model, in accordance with anembodiment of the present principles; and

FIG. 5 shows another exemplary method 500 for estimating a receipt timeof a reply email using a recipient behavior model, in accordance with anembodiment of the present principles.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The present principles are directed to estimating the time until a replyemail will be received using a recipient behavior model.

The present principles provide the sender of an email with an estimateof the time until a reply email will be received (if applicable). Foreach recipient of a new email, the present principles use the body ofthe current email and a model of recipient behavior to estimate the timeat which a reply email will be received. The provision of suchinformation is a form of expectation management; if the sender knows howlong he or she must wait, they will be less frustrated. This informationalso allows the sender to better plan their activities and, ifnecessary, take additional action if the expected response time isunacceptable.

FIG. 1 shows an exemplary processing system 100 to which the presentprinciples may be applied, in accordance with an embodiment of thepresent principles. The processing system 100 includes at least oneprocessor (CPU) 102 operatively coupled to other components via a systembus 104. A read only memory (ROM) 106, a random access memory (RAM) 108,a display adapter 110, an I/O adapter 112, a user interface adapter 114,and a network adapter 198, are operatively coupled to the system bus104.

A display device 116 is operatively coupled to system bus 104 by displayadapter 110. A disk storage device (e.g., a magnetic or optical diskstorage device) 118 is operatively coupled to system bus 104 by I/Oadapter 112.

A mouse 120 and keyboard 122 are operatively coupled to system bus 104by user interface adapter 214. The mouse 120 and keyboard 122 are usedto input and output information to and from system 100.

A transceiver 196 is operatively coupled to system bus 104 by networkadapter 198.

Of course, the processing system 100 may also include other elements(not shown), as readily contemplated by one of skill in the art, as wellas omit certain elements. For example, system 200 described below withrespect to FIG. 2 and system 300 described below with respect to FIG. 3are respective systems for implementing respective embodiments of thepresent principles. Part or all of processing system 100 may beimplemented in one or more of the elements of system 200. Also, part orall of processing system 100 may be implemented in one or more of theelements of system 300.

Moreover, it is to be appreciated that processing system 100 may performat least part of the methods described herein including, for example,parts of method 400 of FIG. 4 and/or parts of method 500 of FIG. 5.

FIG. 2 shows an exemplary system 200 for estimating a receipt time of areply email using a recipient behavior model, in accordance with anembodiment of the present principles. The system 200 includes an emailparser 210, a recipient behavior model (RBM) 220, and a reply emailreceipt time estimate and accuracy/confidence measure indicator(hereinafter “reply email receipt time estimate indicator” in short)230.

The email parser 210 parses emails to determine certain informationtherefrom. Examples of such information include, but are not limited to,date sent, sender, co-recipients, recipient calendar, sender-receiverrelationship information, message attributes, number of unread emails inthe recipient's email inbox, and so forth. One or more of the precedingcan be input to the RBM 220 as described in further detail herein below.

In the embodiment of FIG. 2, the email parser 210 is connected to anemail system 299. Accordingly, the emails from (e.g., the inbox of) theemail system 299 are parsed by the email parser 210.

In an embodiment, emails can simply be provided directly to the emailparser, for example, in a personal storage table (PST) file or similarfile, without the need for the email parser 210 to be connected to emailsystem 299. For example, the same may be provided using a universalserial bus (USB) key or some other connectable memory medium or may betransmitted to the email parser using wired and/or wirelesscommunication technologies, thus obviating the need for a directconnection between the email parser 210 and the email system 299.

The RBM 220 receives, from the email parser 210, one or more of theabove-identified types of information as an input thereto. The RBM 220processes the received input(s) to generate an estimate therefrom of atime of receipt of a reply email. The estimate is output from the RBM220. In an embodiment, the RBM 220 can also output an indication of theaccuracy/confidence of the estimate.

In an embodiment, the estimate and the accuracy/confidence level aredetermined by the RBM 220 either statistically and/or analytically. Inan embodiment, the RBM is generated by a machine learning device 221. Inan embodiment, the machine learning device 221 includes a neural networksystem 222 and a decision tree induction system 223. Of course, otherembodiments can include one of the neural network system 222 and thedecision tree system 223.

Moreover, in an embodiment, the RBM 220 can update an estimatepreviously output therefrom in order to provide an updated estimate. Theupdated estimate is presumably more accurate than the previous estimateit replaces. The updated estimate can be based on, for example,additional information that is acquired and evaluated or can simply bebased on the same information being further evaluated over time.

The RBM generates the estimate responsive to a request for the same. Inthe embodiment of FIG. 2, that request is provided through the emailsystem 299. In other embodiments, the request may be provided directlyto the RBM 220 or through another entity (other than email system 299)to the RBM 220. Moreover, the request can be automatically generated.

An updated estimate can be generated responsive to an update trigger. Inthe embodiment of FIG. 2, that trigger is provided through the emailsystem 299. In other embodiments, the trigger may be provided directlyto the RBM 220 or through another entity (other than email system 299)to the RBM 220. The trigger may include various types of information, asdescribed in further detail herein, or an explicit request by the userfor an updated estimate.

The reply email receipt time estimate indicator 230 indicates to theuser the receipt time estimate for a reply email. The reply emailreceipt time estimate indicator 230 can be, and/or otherwise include, adisplay device, a speaker, a widget, and so forth.

While shown as being separate from the email system 299, it is to beappreciated that one or more of the elements of system 200 can beimplemented in email system 299. For example, the reply email receipttime estimate indicator 230 can be part of the email system 299 and thusprovide an indication of the estimate to the user using the same userinterface that the user uses for emailing. To elaborate further, suchindication may be provided within a window or field of an existing userinterface 291 in email system 299. Given the teachings of the presentprinciples provided herein, these and other variations are readilycontemplated by one of ordinary skill in the art, while maintaining thespirit of the present principles. In other embodiments, a desktop widgetmay be used to indicate the estimate to the user. The precedingimplementations of the indicator 230 are merely illustrative and, thus,other types of indicators can also be used in accordance with theteachings of the present principles, while maintaining the spirit of thepresent principles.

It is to be appreciated that system 200 may perform at least part of themethods described herein including, for example, parts of method 400 ofFIG. 4.

FIG. 3 shows another exemplary system 300 for estimating a receipt timeof a reply email using a recipient behavior model, in accordance with anembodiment of the present principles. The system 300 includes an emailparser 310, a set of recipient behavior models (RBMs) 320A-N, and areply email receipt time estimate and accuracy/confidence measureindicator (hereinafter “receipt time estimate indicator” in short) 330.

In the embodiment of FIG. 3, the set of RBMs 320A-N are collectivelyused to generate an estimate of the time of receipt of a reply email, anupdated estimate, and accuracy/confidence measures for the estimates. Inthe embodiment of FIG. 3, the inputs to each RBM can also include aprevious estimate. In the embodiment of FIG. 3, the set of recipientbehavior models are configured in a master-slave configuration, and amaster recipient behavior model from among the recipient behavior modelsin the set is used to output the estimate that is indicated to the user.

In the embodiment of FIG. 3, a master RBM 320A from the set of RBMs320A-N is used to finally generate/refine an estimate. In an embodiment,the master RBM 320A is the RBM that is local to the individualrequesting the estimate. Of course, other ones of the RBMs can also beused as the master RBM. For example, in other embodiments, the masterRBM is whichever one has information about the recipient, or the mostinformation, or the highest accuracy/confidence value for this or aprevious estimate for the same recipient. The master RBM 320A uses oneor more estimates and/or other information output from one or more ofRBM 320B through RBM 320N to generate the estimate that is thenindicated to the user via the reply email receipt time estimateindicator 330.

In an embodiment, the RMBs 320A-N can be configured in a parallelconfiguration. In such a case, more than one of the RBMs can generate anestimate. A tie breaker or other selection device (not shown) can beused to determine which estimate is to be used. The tie breaker or otherselection device can choose a particular estimate or average more thanone estimate, and so forth.

The RBMs in the set can all be local with respect to a single emailsender. Alternatively, the RBMs can include one or more RBMs that havebeen received from other email senders and/or recipients such that anydata generated by the others can be used to aid in the generation of thecurrent estimate for the current sender.

While a single email parser 310 is shown in FIG. 3 supplying each of theRBM 320A through RBM 320N with certain information, in otherembodiments, more than one email parser may used. For example, each RBMor subset of RBMs may have its own email parser. Such email parsers mayreside at the location of each group member, where the models can betransmitted to (shared with) other group members.

It is to be appreciated that system 300 may perform at least part of themethods described herein including, for example, parts of method 400 ofFIG. 4.

FIG. 4 shows an exemplary method 400 for estimating a receipt time of areply email using a recipient behavior model, in accordance with anembodiment of the present principles.

At step 405, a set of previously sent emails is parsed (e.g., by theparser 210) to extract certain information therefrom. The certaininformation can include, but is not limited to, date sent, sender,co-recipients, recipient calendar, sender-receiver relationshipinformation, message attributes, number of unread emails in therecipient's email inbox, and so forth. In an embodiment, thisinformation is considered training data.

At step 410, the extracted information is input to the recipientbehavior model (RBM).

At step 415, an email is sent to a recipient.

At step 420, a request is received for an estimate of a receipt time ofa reply email to be sent in response to the email of step 415.

At step 425, the RBM generates the estimate of the receipt time of thereply email by applying machine learning (e.g., using machine learningdevice 221) to at least the body of the current email (sent at step 415)and the information input to the RBM at step 410. The machine learningcan include and/or otherwise involve neural networks (e.g., using neuralnetwork system 222) and/or decision tree induction (e.g., using decisiontree induction system 223).

At step 430, the estimate is indicated to a user.

At step 435, the user is afforded an opportunity to send the underlyinginformation in the email of step 415 to the intended recipient usinganother email address or another communication method altogether. Thisopportunity allows the user to try to communicate with the recipient ina different manner, if the estimated time of reply is too great and/orotherwise unacceptable to the original email sender. While only shown inmethod 400 with respect to step 435, it is to be appreciated that suchopportunity can be afforded the original email sender each time he orshe receives an estimate, an updated estimate, or an accuracy/confidencemeasure.

At step 435, an accuracy/confidence measure for the estimate isdetermined and indicated to the user.

At step 440, given the further processing over time of the informationreceived by the RBM 220, an updated estimate is generated and indicatedto the user.

At step 445, additional information is received by the RBM 220.

At step 450, given the additional information received and processed bythe RBM 220, another updated estimate is generated and indicated to theuser.

At step 455, the actual reply email is received (in response to theemail of step 415).

At step 460, the actual reply time of the reply email is compared to theestimate to ascertain any difference there between.

At step 465, the difference, if any, is provided as another input to themodel.

At step 470, the model is updated based on the difference.

FIG. 5 shows another exemplary method 500 for estimating a receipt timeof a reply email using a recipient behavior model, in accordance with anembodiment of the present principles. In an embodiment, the method 500is performed with respect to a predefined group of individuals who shareemails.

At step 505, a set of previously sent emails is parsed (e.g., by theparser 210) to extract certain information therefrom. The certaininformation can include, but is not limited to, date sent, sender,co-recipients, recipient calendar, sender-receiver relationshipinformation, message attributes, number of unread emails in therecipient's email inbox, and so forth. In an embodiment, thisinformation is considered training data.

At step 510, the extracted information is input to the recipientbehavior model (RBM).

At step 515, an email is sent to a recipient.

At step 520, one or more RBMs are received from members of the group.The RBMs from the same group member correspond to different recipients.However, if more than one group member sends an RBM, there is a chancethat two RBMs are sent that correspond to a same recipient.

At step 525, a request is received for an estimate of a receipt time ofa reply email to be sent in response to the email of step 515.

At step 530, one or more of the RBMs, working together if more than one,generate the estimate of the receipt time of the reply email by applyingmachine learning (e.g., using machine learning device 221) to at leastthe body of the current email (sent at step 515) and the informationinput to the RBM at step 510. The machine learning can include and/orotherwise involve neural networks (e.g., using neural network system222) and/or decision tree induction (e.g., using decision tree inductionsystem 223).

At step 535, the estimate is indicated to a user.

While not shown for the sake of brevity, method 500 may include stepssimilar to steps 435-470 from method 400 of FIG. 4, to afford to a useran opportunity to send the underlying information in the email of step515 to the intended recipient using another email address or anothercommunication method altogether, to generate updated estimates, togenerate accuracy/confidence measures for the estimates, and to updatethe RMB(s).

The present principles include a recipient behavior model (RBM). Inputsto the RBM can include, but are not limited to, one or more of thefollowing: date sent (i.e., time of day, day of week, time zone, etc.);estimated date of receipt (should be the same as above, but could bedelayed by transmission); sender; co-recipients (i.e., others CC'd);recipient calendar; sender-receiver relationship information (e.g.,friend, manager, client); message attributes; length; number ofquestions; importance/urgency; topic; subject; keywords; tone;signature; presence of attachments; and number of unread emails inrecipient's email in-box.

The output of the model is the aforementioned time until a reply emailis received by the original sender. In an embodiment, the model can alsooutput an indication of the accuracy/confidence of the estimate.

There are many ways to construct the RBM, for example, statisticallyand/or analytically. Our preferred embodiment employs machine learningto acquire an understanding of the relationship between the inputattributes and the output value(s). However, it is to be appreciatedthat the present principles are not limited to machine learning and,thus, other mechanisms and so forth may be used to construct the RBMwhile maintaining the spirit of the present principles.

Two popular and exemplary machine learning techniques to which thepresent principles can be applied are neural networks and decision treeinduction. Both of the preceding machine learning techniques couldeasily be applied here by one skilled in the art. To use the machinelearning approach, access to a set of training examples is desired.These examples are easily obtained from previous email correspondences.A new example could be derived for each email. Even those which were notsent as a reply to a previous email are useful as they provideinformation about the times when that person is likely to send. Most ofthe above inputs are easily extracted from individual messages. The morecomplex message attributes, such as topic and number of questions, arecomputed after analyzing the text.

The recipient model can be built offline (i.e., the learning processneed not be re-run for every new email). However, periodic updates areuseful. Furthermore, the system can compare the estimates to actualreply times to determine when the model needs to be re-learnt.

The present principles include a variety of mechanisms for the timingwith which this information is presented to the sender. The estimatecould be updated after one of the following, for example: (1) anykeystroke while the message body has focus, focus being a computing termindicating the user interface element that is currently in theforeground and or is currently receiving input from the user; (2) a newrecipient is added/removed; 3) the message body loses focus; and (4) thesender clicks a button requesting the estimate.

The model would be used to update the estimate each time one of theinputs changes. For example, if the manager of one recipient was addedto the CC list, then a new estimate of the time to reply should becomputed.

In an embodiment, the email correspondences from which the trainingexamples can be extracted include, but are not limited to, one or moreof the following: (1) personal correspondences between the sender andthe recipient; and (2) peer correspondences. Regarding personalcorrespondences, in an embodiment, these would be stored on the sendersystem and trivially accessible to the machine learning algorithm.Regarding peer correspondence, in an embodiment, trusted peers (e.g.,work colleagues) could make their emails available for training exampleextraction. Alternatively, peers could forward only the sub-set of metadata which they feel comfortable revealing, and only for nominatedrecipients.

In an embodiment, there is also the possibility of directly exchangingrecipient models, including models produced by the consumer themselves.At the recipient end these models can be merged into an ensemble ofestimators.

As will be appreciated by one skilled in the art, aspects of the presentprinciples may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present principles may take theform of an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present principles may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent principles may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present principles are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present principles. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

Reference in the specification to “one embodiment” or “an embodiment” ofthe present principles, as well as other variations thereof, means thata particular feature, structure, characteristic, and so forth describedin connection with the embodiment is included in at least one embodimentof the present principles. Thus, the appearances of the phrase “in oneembodiment” or “in an embodiment”, as well any other variations,appearing in various places throughout the specification are notnecessarily all referring to the same embodiment.

It is to be appreciated that the use of any of the following “/”,“and/or”, and “at least one of”, for example, in the cases of “A/B”, “Aand/or B” and “at least one of A and B”, is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of both options (A andB). As a further example, in the cases of “A, B, and/or C” and “at leastone of A, B, and C”, such phrasing is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of the third listedoption (C) only, or the selection of the first and the second listedoptions (A and B) only, or the selection of the first and third listedoptions (A and C) only, or the selection of the second and third listedoptions (B and C) only, or the selection of all three options (A and Band C). This may be extended, as readily apparent by one of ordinaryskill in this and related arts, for as many items listed.

Having described preferred embodiments of a system and method (which areintended to be illustrative and not limiting), it is noted thatmodifications and variations can be made by persons skilled in the artin light of the above teachings. It is therefore to be understood thatchanges may be made in the particular embodiments disclosed which arewithin the scope of the invention as outlined by the appended claims.Having thus described aspects of the invention, with the details andparticularity required by the patent laws, what is claimed and desiredprotected by Letters Patent is set forth in the appended claims.

What is claimed is:
 1. A system, comprising: a set of recipient behaviormodels for generating an estimate of a receipt time of a reply emailfrom a recipient of an initial email using machine learning; and anindicator device for indicating the estimate to a user, wherein the setof recipient behavior models is shared among a set of trusted peers, andwherein the set of recipient behavior models is configured in amaster-slave configuration, and a master recipient behavior model fromamong the recipient behavior models in the set is used to output theestimate that is indicated to the user.
 2. The system of claim 1,wherein said recipient behavior model generates a confidence measure ofthe estimate.
 3. The system of claim 1, further comprising a receiverfor receiving at least one of the recipient behavior models in the setfrom a trusted peer, and wherein the more than one recipient behaviormodel used to generate the estimate comprises the at least one of therecipient behavior models received from the trusted peer.
 4. The systemof claim 1, wherein the set of recipient behavior models generates theestimate by applying the machine learning to the initial email and totraining data from other emails.
 5. The system of claim 4, wherein themachine learning comprises at least one of neural networks and decisiontree induction.
 6. A computer program product for providing an estimateof a receipt time of a reply email from a recipient of an initial email,the computer program product comprising a non-transitory computerreadable storage medium having program code embodied therewith, theprogram code executable by a computer to perform a method comprising:sharing a set of recipient behavior models among a set of trusted peers;generating the estimate of the receipt time of the reply email from therecipient of the initial email using more than one recipient behaviormodel from the set of recipient behavior models and machine learning;and indicating the estimate to a user using an indication device,wherein the set of recipient behavior models is configured in amaster-slave configuration, and a master recipient behavior model fromamong the recipient behavior models in the set is used to output theestimate that is indicated to the user.
 7. The computer program productof claim 6, wherein at least one previous actual reply time is used togenerate the estimate.
 8. The computer program product of claim 6,wherein the method further comprises generating a confidence measure ofthe estimate.
 9. The computer program product of claim 6, wherein saidsharing step comprises receiving at least one of the recipient behaviormodels in the set from a trusted peer, and wherein the more than onerecipient behavior model used to generate the estimate comprises the atleast of the recipient behavior models received from the trusted peer.10. The computer program product of claim 6, wherein the more than onerecipient behavior model generates the estimate by applying the machinelearning to the initial email and to training data from other emails.11. The computer program product of claim 10, wherein the training datacomprises at least one of a date sent, a sender, co-recipients, arecipient calendar, sender-receiver relationship information, messageattributes, and a number of unread emails in an email inbox of therecipient.
 12. The computer program product of claim 10, wherein themachine learning comprises at least one of neural networks and decisiontree induction.
 13. The computer program product of claim 10, whereinthe method further comprises generating an updated estimate based on atleast one of additional training data that is newly acquired andevaluated and the same training data being further evaluated over time.14. The computer program product of claim 13, wherein the updatedestimate is generated responsive to a new recipient being added or anexisting recipient being removed from the initial email.
 15. Thecomputer program product of claim 13, wherein the updated estimate isgenerated responsive to a user request for the updated estimate.
 16. Thecomputer program product of claim 10, wherein the training data isobtained from previous personal correspondence between a sender of theinitial email and the recipient of the initial email.
 17. The computerprogram product of claim 10, wherein the training data is obtained fromprevious peer correspondence between trusted peers, the trusted peersincluding the recipient of the initial email.
 18. The computer programproduct of claim 10, wherein the training data represents a subset ofdata corresponding to the previous peer correspondence, the subsetdetermined to preserve private details in the training data.